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Molybdenum disulfide (MoS 2 ), a notable two-dimensional (2D) material, has attracted considerable interest for its potential applications in gas sensing, despite its typically insulating characteristics, which have limited its practical use. In this study, we present the use of mixed phase MoS 2 (1T@2H-MoS 2 ) to overcome sensing limitations of MoS 2 material by enhancing its conductivity and demonstrating its high-performance characteristics for sensing ammonia (NH 3 ) at room temperature (20 °C). The 1T@2H-MoS 2 was synthesized via a hydrothermal process, and the coexistence of two different phases (the 1T and 2H phases) was confirmed by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Raman spectroscopy. The flower-like morphology was confirmed by field emission scanning electron microscopy (FESEM) and TEM. Our results indicate that the presence of both 1T and 2H phases within the material introduces sulfur vacancies, which we propose are critical to significantly enhancing its sensitivity to NH 3 gas. The ammonia-sensing performance of the 1T@2H-MoS 2 material was evaluated, and it demonstrated rapid and selective detection of NH 3 gas across a wide concentration range (2 ppm to 100 ppm), with a very swift response time (7 s), fast recovery and high selectivity at room temperature without requiring heating. This improvement is attributed to the increased conductivity and effective active sites provided by the sulfur defects. This study underscores the potential of mixed-phase MoS 2 in developing rapidly responsive and highly selective NH 3 sensors, paving the way for the safety monitoring of hazardous gases in various industrial settings.

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients' exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2-3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8-10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.